UnLynx: A Decentralized System for Privacy-Conscious Data Sharing

Current solutions for privacy-preserving data sharing among multiple parties either depend on a centralized authority that must be trusted and provides only weakest-link security (e.g., the entity that manages private/secret cryptographic keys), or leverage on decentralized but impractical approaches (e.g., secure multi-party computation). When the data to be shared are of a sensitive nature and the number of data providers is high, these solutions are not appropriate. Therefore, we present UnLynx, a new decentralized system for efficient privacypreserving data sharing. We consider m servers that constitute a collective authority whose goal is to verifiably compute on data sent from n data providers. UnLynx guarantees the confidentiality, unlinkability between data providers and their data, privacy of the end result and the correctness of computations by the servers. Furthermore, to support differentially private queries, UnLynx can collectively add noise under encryption. All of this is achieved through a combination of a set of new distributed and secure protocols that are based on homomorphic cryptography, verifiable shuffling and zero-knowledge proofs. UnLynx is highly parallelizable and modular by design as it enables multiple security/privacy vs. runtime tradeoffs. Our evaluation shows that UnLynx can execute a secure survey on 400,000 personal data records containing 5 encrypted attributes, distributed over 20 independent databases, for a total of 2,000,000 ciphertexts, in 24 minutes.

Privacy-Preserving Federated Analytics using Multiparty Homomorphic Encryption

David Jules Froelicher

Analyzing and processing data that are siloed and dispersed among multiple distrustful stakeholders is difficult and can even become impossible when the data are sensitive or confidential. Current data-protection and privacy regulations highly restrict the sharing and outsourcing of personal information among stakeholders that are in different jurisdictions. Sharing data is, however, required in many domains. The medical sector is a paradigmatic example: Privacy is paramount and data sharing is needed in numerous applications where data is scarce and scattered among multiple stakeholders around the world. Existing privacy-preserving solutions for federated analytics (FA) rely either (1) on data centralization or outsourcing to a limited number of entities, which incur multiple security and trust issues, or (2) on the exchange of cleartext aggregated and optionally obfuscated data, which can leak personal information or introduce bias in the final result. In this thesis, our goal is (1) to propose privacy-preserving federated solutions for exploration, and for statistical and machine-learning analyses on data held by multiple distrustful stakeholders, and (2) to analyze and evaluate the proposed systems, thus showing that they provide an efficient, secure, scalable, and accurate alternative to existing solutions for FA by proving their utility in real-world state-of-the-art biomedical studies. We rely on multiparty homomorphic encryption (MHE). MHE combines secure multiparty computation (SMC) techniques with homomorphic encryption (HE) by pooling the advantages of both SMC and HE, i.e., interactivity and flexibility, and by minimizing their disadvantages, i.e., difficulty in scaling to a large number of parties and computation complexity.First, we design UnLynx, a system that enables privacy-preserving federated data exploration on a distributed dataset held by multiple data-providers (DPs), where N-1 out of N of the nodes performing the computations can be malicious. We build interactive protocols by relying on ElGamal additive homomorphic encryption (AHE) and ensure that each untrusted-node operation can be publicly verified by means of zero-knowledge proofs (ZKPs). We then explore how statistics, e.g., standard deviation and variance, can be computed by relying on AHE and ZKPs through the design of another system named Drynx. In Drynx, we also explore how to limit the influence of an entity that inputs wrong data in the system, and we propose an efficient federated solution for correctness verification.We propose Spindle a solution for secure cooperative gradient descent on federated data that we instantiate for the privacy-preserving training and oblivious evaluation of generalized linear models. Spindle covers the entire machine-learning workflow, as it enables oblivious predictions to be performed on a trained model that remains secret. It ensures both data and model confidentiality in a passive adversarial model in which N-1 out of N DPs can collude.Finally, we demonstrate that the solutions proposed in this thesis can be efficient enablers for large-scale, sensitive, multi-site biomedical studies. We design and test, by replicating recent medical studies, secure workflows for the federated execution of computations that span from analyses with low computational complexity, such as survival analyses, to analyses with high computational complexity such as genome-wide association studies on millions of variants.

EPFL2021

Implications of Position in Cryptography

Handan Kilinç Alper

In our daily lives, people or devices frequently need to learn their location for many reasons as some services depend on the absolute location or the proximity. The outcomes of positioning systems can have critical effects e.g., on military, emergency. Thus, the security of these systems is quite important. In this thesis, we concentrate on many security aspects of position in cryptography. The first part of this thesis focuses on the theory of distance bounding. A distance bounding protocol is a two-party authentication protocol between a prover and a verifier which considers the distance of the prover as a part of his/her credential. It aims to defeat threats by malicious provers who try to convince that they are closer to the verifier or adversaries which seek to impersonate a far-away prover. In this direction, we first study the optimal security bounds that a distance bounding protocol can achieve. We consider the optimal security bounds when we add some random delays in the distance computation and let the prover involve distance computation. Then, we focus on solving the efficiency problem of public-key distance bounding because the public-key cryptography requires much more computations than the symmetric-key cryptography. We construct two generic protocols (one without privacy, one with) which require fewer computations on the prover side compared to the existing protocols while keeping the highest security level. Then, we describe a new security model involving a tamper-resistant hardware. This model is called the secure hardware model (SHM). We define an all-in-one security model which covers all the threats of distance bounding and an appropriate privacy notion for SHM. The second part of this thesis is to fill the gap between the distance bounding and its real-world applications. We first consider contactless access control. We define an integrated security and privacy model for access control using distance bounding (DB) to defeat relay attacks. We show how a secure DB protocol can be converted to a secure contactless access control protocol. Regarding privacy (i.e., keeping anonymity in a strong sense to an active adversary), we show that the conversion does not always preserve privacy, but it is possible to study it on a case by case basis. Then, we consider contactless payment systems. We design an adversarial model and define formally the contactless payment security against malicious cards and malicious terminals. Accordingly, we design a contactless payment protocol and show its security in our security model. The last part of this thesis focuses on positioning. We consider two problems related to positioning systems: localization and proof of location. In localization, a user aims to find its position by using a wireless network. In proof of location, a user wants to prove his/her position e.g., to have access to a system or authorize itself. We first formally define the problem of localization and construct a formal security model. We describe algorithms and protocols for localization which are secure in our model. Proof of location has been considered formally by Chandran et al. in CRYPTO 2009 and it was proved that achieving security is not possible in the vanilla model. By integrating the localization and the secure hardware model, we obtain a model where we can achieve proof of location.

EPFL2018

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets

David Jules Froelicher, Joao André Gomes de Sá E Sousa, Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza

Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, sharing personal information requires individuals' unconditional consent or is often simply forbidden for privacy and security reasons. In this paper, we propose Drynx, a decentralized system for privacy-conscious statistical analysis on distributed datasets. Drynx relies on a set of computing nodes to enable the computation of statistics such as standard deviation or extrema, and the training and evaluation of machine-learning models on sensitive and distributed data. To ensure data confidentiality and the privacy of the data providers, Drynx combines interactive protocols, homomorphic encryption, zero-knowledge proofs of correctness, and differential privacy. It enables an efficient and decentralized verification of the input data and of all the system's computations thus provides auditability in a strong adversarial model in which no entity has to be individually trusted. Drynx is highly modular, dynamic and parallelizable. Our evaluation shows that it enables the training of a logistic regression model on a dataset (12 features and 600,000 records) distributed among 12 data providers in less than 2 seconds. The computations are distributed among 6 computing nodes, and Drynx enables the verification of the query execution's correctness in less than 22 seconds.